Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2602.19659v1</id>\n    <title>Curiosity Over Hype: Modeling Motivation Language to Understand Early Outcomes in a Selective Quantum Track</title>\n    <updated>2026-02-23T10:09:05Z</updated>\n    <link href='https://arxiv.org/abs/2602.19659v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2602.19659v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>We study whether latent motivation signals in short Spanish admission responses predict engagement and performance in an early quantum computing pathway run by QuantumHub Peru. We analyze N=241 applicants' open responses and link them to outcomes from two selective modules: Module 1 (secondary; mathematics and computing foundations; n=23) and Module 2 (secondary + early undergraduate; quantum fundamentals; n=36, including M1 continuers). To ensure baseline comparability, the M2 university entrance exam matched the difficulty of the M1 final. Final grades followed the program's official cohort-specific weightings (attendance/assignments/exam), which we retain to preserve ecological validity. Methodologically, we model text with Latent Dirichlet Allocation (LDA, k=8) and, for robustness, with sentence embeddings from a small multilingual language model, EmbeddingGemma-300M, projected via UMAP and clustered with HDBSCAN. This combination leverages the transparency of bag-of-words topics and the semantic richness of small language model embeddings. Descriptively, curiosity/learning topics show higher grades and attendance than technology/career-oriented topics; inferential tests are underpowered (e.g., linear R2 ~ 0.03; logistic pseudo-R2 ~ 0.04) so effect-size estimates should be viewed as preliminary rather than confirmatory. Embedding-based clustering yields seven clusters with 11.2% noise and modest agreement with LDA (ARI=0.068; NMI=0.163). Results suggest that brief motivation responses encode promising signals that could support early mentoring in rigorous STEM pipelines, while highlighting the need for larger, pre-registered studies.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='physics.ed-ph'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='quant-ph'/>\n    <published>2026-02-23T10:09:05Z</published>\n    <arxiv:comment>Published in the Proceedings of IEEE ICALTER 2025. 5 pages, 7 figures</arxiv:comment>\n    <arxiv:primary_category term='physics.ed-ph'/>\n    <arxiv:journal_ref>Proceedings of the IEEE International Conference on Advanced Learning Technologies (ICALTER), 2025</arxiv:journal_ref>\n    <author>\n      <name>Daniella Alexandra Crysti Vargas Saldana</name>\n    </author>\n    <author>\n      <name>Freddy Herrera Cueva</name>\n    </author>\n    <arxiv:doi>10.1109/ICALTER69698.2025.11355072</arxiv:doi>\n    <link href='https://doi.org/10.1109/ICALTER69698.2025.11355072' rel='related' title='doi'/>\n  </entry>"
}